Aiming at the problem of poor pedestrian detection effect in complex scenes, a pedestrian detection algorithm based on improved Mask R-CNN framework was proposed with the use of the leading research results in deep learning-based object detection. Firstly, K-means algorithm was used to cluster the object frames of the pedestrian datasets to obtain the appropriate aspect ratio. By adding the set of aspect ratio (2:5), 12 anchors were able to be adapted to the size of the pedestrian in the image. Secondly, combined with the technology of fine-grained image recognition, the high accuracy of pedestrian positioning was realized. Thirdly, the foreground object was segmented by the Full Convolutional Network (FCN), and pixel prediction was performed to obtain the local mask (upper body, lower body) of the pedestrian, so as to achieve the fine-grained detection of pedestrians. Finally, the overall mask of the pedestrian was obtained by learning the local features of the pedestrian. In order to verify the effectiveness of the improved algorithm, the proposed algorithm was compared with the current representative object detection methods (such as Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv2 and R-FCN (Region-based Fully Convolutional Network)) on the same dataset. The experimental results show that the improved algorithm increases the speed and accuracy of pedestrian detection and reduces the false positive rate.

This paper proposed a new best viewpoints selection approach that was capable of selecting best viewpoints for 3D models based on a feature points detection process. First, a new saliency measure was defined to compute the saliency of 3D meshes vertices, which assumed that the saliency of a given vertex on a 3D model could be described by its average difference of distances within a local space. Then, the effective feature points were promisingly able to be extracted based on vertices saliency. Finally, a simple selection strategy was adopted to determine the best viewpoints for 3D mesh models. The quality of viewpoints was a combination of the geometirc distribution and the saliency of visible feature points. The experimental results validate the effectiveness of the proposed approach, which can measure viewpoint quality objectively and obtain the best viewpoints of good visual effect.

To solve the problem of poor self-adaptive ability in the robot obstacle avoidance, combined with evolution thought of Genetic Algorithm (GA), an Evolutionary Operant Behavior Learning Model (EOBLM) was proposed for the mobile robot learning obstacle avoidance in unknown environment, which was based on Operant Conditioning (OC) and Adaptive Heuristic Critic (AHC) learning. The proposed model was a modified version of the AHC learning architecture. Adaptive Critic Element (ACE) network was composed of a multi-layer feedforward network and the learning was enhanced by TD(λ) algorithm and gradient descent algorithm. A tropism mechanism was designed in this stage as intrinsic motivation and it could direct the orientation of the Agent learning. Adaptive Selection Element (ASE) network was used to optimize operant behavior to achieve the best mapping from state to actor. The optimizing process has two stages. At the first stage, the information entropy got by OC learning algorithm was used as individual fitness to search the optimal individual with executing the GA learning. At the second stage, the OC learning selected the optimal operation behavior within the optimal individual and got new information entropy. The results of experiments on obstacle avoidance show that the method endows the mobile robot with the capabilities of learning obstacle avoidance actively for path planning through interaction with the environment constantly. The results were compared with the traditional AHC learning algorithm, and the proposed model had better performance on self-learning and self-adaptive abilities.

To decrease the storage space and improve the search velocity of CRL(Certificate Revocation List),a bit pointer was used to shorten the certificate number of it.And a new certificate recocation tree was proposed,which could keep the good properties of CRT(Certificate Revocation Tree) that is easy to check or prove whether a certificate is revoked or not,the check only need the related path values but not the whole CRT values.The new tree also could overcome the disadvantage of CRT that any update will cause the whole CRT to be computed,so it accelerate the speed of the CRT update.